# this cell is tagged as parameters for `papermill` parameterization
# input configs
altair_config = None
nipah_config = None

# input files
binding_E2_file = None
binding_E3_file = None

# output images
entry_binding_combined_corr_plot = None
entry_binding_combined_corr_plot_agg = None
E2_E3_correlation = None
E2_E3_correlation_site = None
binding_by_site_plot = None
entry_binding_corr_heatmap = None
binding_corr_heatmap = None
binding_region_bubble_plot = None
combined_contact_ranked_bar_output = None

# Parameters
nipah_config = "nipah_config.yaml"
altair_config = "data/custom_analyses_data/theme.py"
binding_E2_file = "results/filtered_data/binding/e2_binding_filtered.csv"
binding_E3_file = "results/filtered_data/binding/e3_binding_filtered.csv"
entry_binding_combined_corr_plot = (
    "results/images/entry_binding_combined_corr_plot.html"
)
entry_binding_combined_corr_plot_agg = (
    "results/images/entry_binding_combined_corr_plot_agg.html"
)
E2_E3_correlation = "results/images/E2_E3_correlation.html"
E2_E3_correlation_site = "results/images/E2_E3_correlation_site.html"
binding_by_site_plot = "results/images/binding_by_site_plot.html"
entry_binding_corr_heatmap = "results/images/entry_binding_corr_heatmap.html"
binding_corr_heatmap = "results/images/binding_corr_heatmap.html"
binding_region_bubble_plot = "results/images/binding_region_bubble_plot.html"
combined_contact_ranked_bar_output = (
    "results/images/combined_contact_ranked_bar_output.html"
)

import math
import os
import re
import altair as alt
import numpy as np
import pandas as pd
import scipy.stats
import yaml

# allow more rows for Altair
_ = alt.data_transformers.disable_max_rows()

if (
    os.getcwd()
    == "/fh/fast/bloom_j/computational_notebooks/blarsen/2023/Nipah_Malaysia_RBP_DMS/"
):
    pass
    print("Already in correct directory")
else:
    os.chdir(
        "/fh/fast/bloom_j/computational_notebooks/blarsen/2023/Nipah_Malaysia_RBP_DMS/"
    )
    print("Setup in correct directory")

Setup in correct directory

if nipah_config is None:
    ##hard paths in case don't want to run with snakemake
    print("loading hard paths")
    altair_config = "data/custom_analyses_data/theme.py"
    nipah_config = "nipah_config.yaml"

    # input files
    binding_E2_file = "results/filtered_data/binding/e2_binding_filtered.csv"
    binding_E3_file = "results/filtered_data/binding/e3_binding_filtered.csv"

if altair_config:
    with open(altair_config, "r") as file:
        exec(file.read())

with open(nipah_config) as f:
    config = yaml.safe_load(f)

# import binding data
df_E2_filter = pd.read_csv(binding_E2_file)
display(df_E2_filter.head(3))
df_E3_filter = pd.read_csv(binding_E3_file)
display(df_E3_filter.head(3))

## Merge the filtered EFNB2 and EFNB3 DataFrames for combined analysis.
df_binding_effect_merge = pd.merge(
    df_E2_filter,
    df_E3_filter,
    on=["site", "wildtype", "mutant"],
    suffixes=["_E2", "_E3"],
    how="outer",
)
display(df_binding_effect_merge.head(3))
## Add a 'selection' column to distinguish between EFNB2 and EFNB3 data.
df_E2_filter["selection"] = "bEFNB2"
df_E3_filter["selection"] = "bEFNB3"

## Concatenate the DataFrames for plotting or further analysis.
df_binding_effect_concat = pd.concat([df_E2_filter, df_E3_filter])
display(df_binding_effect_concat.head(3))

# What are the top 5 highest and lowest binding mutants for EFNB2 and EFNB3?
def find_highest_lowest(df, name):
    print(f"We are analyzing {name}\n")
    print(f"The total number of mutants was: {df.shape[0]}\n")
    tmp_df = df.sort_values(by="binding_mean")
    print("These are the lowest binding mutants detected:")
    display(tmp_df.head(5))

    tmp_df_high = df.sort_values(by="binding_mean", ascending=False)
    print("\nThese are the highest binding mutants detected:\n")
    display(tmp_df_high.head(5))

    # What about mutants with positive entry scores?
    tmp_df = df[df["effect"] > 0].sort_values(by="binding_mean")
    print("These are the lowest binding mutants detected with positive entry scores:")
    display(tmp_df.head(5))

    tmp_df_high = df[df["effect"] > 0].sort_values(by="binding_mean", ascending=False)
    print(
        "\nThese are the highest binding mutants detected with positive entry scores:\n"
    )
    display(tmp_df_high.head(5))

    mean_df = df.groupby('site')['binding_mean'].sum().reset_index()
    display(mean_df.sort_values(by='binding_mean',ascending=False).head(10))


find_highest_lowest(df_E2_filter, "bEFNB2")
find_highest_lowest(df_E3_filter, "bEFNB3")

We are analyzing bEFNB2

The total number of mutants was: 6804

These are the lowest binding mutants detected:

These are the highest binding mutants detected:

These are the lowest binding mutants detected with positive entry scores:

These are the highest binding mutants detected with positive entry scores:

We are analyzing bEFNB3

The total number of mutants was: 6618

These are the lowest binding mutants detected:

These are the highest binding mutants detected:

These are the lowest binding mutants detected with positive entry scores:

These are the highest binding mutants detected with positive entry scores:

def find_good_binding_for_both(df):
    e2_good = df.sort_values(by='binding_mean_E2',ascending=False)
    e3_good = df.sort_values(by='binding_mean_E3',ascending=False)
    e2_good = e2_good.head(50)
    e3_good = e3_good.head(50)
    combo = pd.merge(e2_good,e3_good,on=['site','wildtype','mutant'],how='inner')
    display(combo)

find_good_binding_for_both(df_binding_effect_merge)

# Compare E2 and E3 binders
def find_highest_lowest(df):
    df["binding_diff"] = (df["binding_mean_E2"] - df["binding_mean_E3"]).abs()
    print(
        "These are the mutants with the biggest difference between EFNB2 and EFNB3:\n"
    )
    display(df.sort_values(by="binding_diff", ascending=False).head(10))

    # calculate aggregate differences
    agg_df = (
        df.groupby("site")[["binding_mean_E2", "binding_mean_E3", "binding_diff"]]
        .mean()
        .reset_index()
    )
    print("These are the sites with the biggest difference between EFNB2 and EFNB3:\n")
    display(agg_df.sort_values(by="binding_diff", ascending=False).head(5))


find_highest_lowest(df_binding_effect_merge)
# find_highest_lowest(df_E3_filter,'EFNB3')

These are the mutants with the biggest difference between EFNB2 and EFNB3:

These are the sites with the biggest difference between EFNB2 and EFNB3:

def plot_corr_binding_entry_updated(df, flag):
    # Define interactive selectors for variant selection.
    # 'variant_selector' is for selecting individual variants based on 'site' and 'mutant'.
    variant_selector = alt.selection_point(
        on="mouseover", nearest=True, empty=False, fields=["site", "mutant"], value=0
    )
    # 'variant_selector_agg' is for aggregated selection based on 'site' only.
    variant_selector_agg = alt.selection_point(
        on="mouseover", nearest=True, empty=False, fields=["site"], value=0
    )

    # Initialize an empty list to store charts for each unique selection in 'df'.
    empty_chart = []
    
    # Loop through each unique cell selection in the DataFrame.
    for cell in list(df["selection"].unique()):
        # Filter DataFrame for the current selection.
        tmp_df = df[df["selection"] == cell]
        
        # Check if aggregation flag is True to aggregate data.
        if flag == True:
            # Aggregate data by 'site', summing 'binding_median' and 'effect', then reset index.
            agg_df = (
                tmp_df.groupby("site")[["binding_mean", "effect"]].mean().reset_index()
            )
            # Create a chart using aggregated data with specified encodings.
            chart = (
                alt.Chart(agg_df)
                .mark_point(stroke="black", filled=True)  # Use filled points with black stroke.
                .encode(
                    x=alt.X(
                        "effect",
                        title=f"Mean entry in CHO-{cell}",
                        axis=alt.Axis(grid=True),
                    ),
                    y=alt.Y(
                        "binding_mean",
                        title=f"Mean {cell} binding",
                        axis=alt.Axis(grid=True),
                    ),
                    # Dynamic opacity, size, strokeWidth, and color based on 'variant_selector_agg'.
                    opacity=alt.condition(
                        variant_selector_agg, alt.value(1), alt.value(0.2)
                    ),
                    size=alt.condition(
                        variant_selector_agg, alt.value(100), alt.value(50)
                    ),
                    strokeWidth=alt.condition(
                        variant_selector_agg, alt.value(1), alt.value(0)
                    ),
                    color=alt.condition(
                        variant_selector_agg, alt.value("orange"), alt.value("black")
                    ),
                    tooltip=["site", "binding_mean", "effect"],
                )
                .add_params(variant_selector_agg)
            )
            # Add the chart to the list.
            empty_chart.append(chart)

        else:
            # Create a chart for individual data points with specified encodings.
            chart = (
                alt.Chart(tmp_df)
                .mark_point(stroke="black", filled=True)
                .encode(
                    x=alt.X(
                        "effect", title=f"Entry in CHO-{cell}", axis=alt.Axis(grid=True)
                    ),
                    y=alt.Y(
                        "binding_mean",
                        title=f"{cell} binding",
                        axis=alt.Axis(grid=True),
                    ),
                    # Dynamic opacity, size, strokeWidth, and color based on 'variant_selector'.
                    opacity=alt.condition(
                        variant_selector, alt.value(1), alt.value(0.1)
                    ),
                    size=alt.condition(variant_selector, alt.value(50), alt.value(20)),
                    strokeWidth=alt.condition(
                        variant_selector, alt.value(1), alt.value(0)
                    ),
                    color=alt.condition(
                        variant_selector, alt.value("orange"), alt.value("black")
                    ),
                    tooltip=[
                        "site",
                        "wildtype",
                        "mutant",
                        "binding_mean",
                        "times_seen_binding",
                        "effect",
                    ],
                )
                .add_params(variant_selector)
            )
            # Add the chart to the list.
            empty_chart.append(chart)

    # Combine all charts horizontally with a title.
    combined_chart = alt.hconcat(
        *empty_chart, title=alt.Title("Correlation between binding and entry")
    )
    
    # Return the combined chart for display or further use.
    return combined_chart

# Generate and display plots for non-aggregated data.
entry_binding_corr_plot = plot_corr_binding_entry_updated(df_binding_effect_concat, False)
entry_binding_corr_plot.display()

# Save the plot 
if entry_binding_combined_corr_plot is not None:
    entry_binding_corr_plot.save(entry_binding_combined_corr_plot)

# Repeat for aggregated data.
entry_binding_corr_plot_agg = plot_corr_binding_entry_updated(df_binding_effect_concat, True)
entry_binding_corr_plot_agg.display()

# Save the aggregated plot
if entry_binding_combined_corr_plot is not None:
    entry_binding_corr_plot_agg.save(entry_binding_combined_corr_plot_agg)

def plot_entry_binding_corr_heatmap(df):
    empty_chart = []

    for cell in list(df["selection"].unique()):
        tmp_df = df[df["selection"] == cell]
        chart = (
            alt.Chart(tmp_df, title=f"{cell}")
            .mark_rect()
            .encode(
                x=alt.X(
                    "effect", title="Cell entry", axis=alt.Axis(values=[-2, -1, 0, 1])
                ).bin(maxbins=50),
                y=alt.Y(
                    "binding_mean",
                    title="Receptor binding",
                    axis=alt.Axis(values=[-4, -2, 0, 2]),
                ).bin(maxbins=50),
                color=alt.Color("count()", title="Count").scale(type='log'),
            )
        ).properties(width=200,height=200)
        empty_chart.append(chart)

    combined_chart = alt.hconcat(
        *empty_chart, 
    ).resolve_scale(y="shared", x="shared", color="shared")
    return combined_chart


entry_binding_corr_heat = plot_entry_binding_corr_heatmap(df_binding_effect_concat)
entry_binding_corr_heat.display()
if entry_binding_combined_corr_plot is not None:
    entry_binding_corr_heat.save(entry_binding_corr_heatmap)

def overall_stats(df, effect, name):
    # Now group sites and find sites where all mutants are deleterious
    filtered_df = df.groupby("site").filter(lambda group: (group[effect] < -0.25).all())
    # Which sites are these?
    unique = filtered_df["site"].unique()
    # Convert unique to a Pandas Series
    unique_series = pd.Series(unique)

    # Find the common elements that are also contact sites
    unique_contact_bool = unique_series.isin(config["contact_sites"])
    # Filter and get the common elements
    common_elements = unique_series[unique_contact_bool]

    print(f"The dataset we are analyzing is: {name}\n")

    # Print the common elements
    print(
        f"Here are the contact sites that only have negative binding scores: {list(common_elements)}\n"
    )

    print(f"There are {len(unique)} sites with all negative binding score mutants\n")
    print(
        f"These are the sites with all negative binding score mutants: {list(unique)}\n"
    )

    # Now find sites with low and high binding (median)
    median_df = (
        df.groupby("site")["binding_mean"]
        .max()
        .reset_index()
        .sort_values(by="binding_mean", ascending=False)
    )
    print("These are the sites with the highest binding mutants:\n")
    display(median_df.head(5))

    # Now calculate mutant number
    total_mutants = df.shape[0]

    mutants_above_cutoff_tolerated = df[df["effect"] > 0]
    mutants_above_cutoff_tolerated = mutants_above_cutoff_tolerated[
        ["site", "effect", "binding_mean", "wildtype", "mutant"]
    ]

    total_sites = df["site"].unique().shape[0]

    print(f"The total number of sites are: {total_sites}")


overall_stats(df_E2_filter, "binding_mean", "EFNB2")
overall_stats(df_E3_filter, "binding_mean", "EFNB3")

The dataset we are analyzing is: EFNB2

Here are the contact sites that only have negative binding scores: [242, 389, 488, 490, 491, 501, 504, 505, 531, 532, 533, 557, 579, 581, 588]

There are 41 sites with all negative binding score mutants

These are the sites with all negative binding score mutants: [100, 116, 220, 236, 238, 242, 243, 248, 346, 351, 352, 389, 390, 400, 435, 438, 441, 460, 467, 486, 487, 488, 490, 491, 494, 495, 497, 501, 504, 505, 526, 531, 532, 533, 557, 579, 581, 584, 585, 588, 590]

These are the sites with the highest binding mutants:

The total number of sites are: 510
The dataset we are analyzing is: EFNB3

Here are the contact sites that only have negative binding scores: [389, 488, 501, 505, 531, 532]

There are 15 sites with all negative binding score mutants

These are the sites with all negative binding score mutants: [108, 352, 389, 467, 486, 488, 494, 495, 497, 501, 505, 510, 531, 532, 584]

These are the sites with the highest binding mutants:

The total number of sites are: 504

# find sites that are different
def find_biggest_differences(df):
    efnb2_good_efnb3_bad = df[
        (df["binding_mean_E2"] > 0.5) & (df["binding_mean_E3"] < -0.5)
    ].sort_values(by="binding_mean_E2", ascending=False)
    print('mutantions good for efnb2 binding, bad for efnb3 binding:\n')
    display(efnb2_good_efnb3_bad)

    efnb2_bad_efnb3_good = df[
        (df["binding_mean_E2"] < -0.5) & (df["binding_mean_E3"] > 0.5)
    ].sort_values(by="binding_mean_E3", ascending=False)
    print('mutantions bad for efnb2 binding, good for efnb3 binding:\n')
    display(efnb2_bad_efnb3_good)


find_biggest_differences(df_binding_effect_merge)

mutantions good for efnb2 binding, bad for efnb3 binding:

mutantions bad for efnb2 binding, good for efnb3 binding:

def plot_entry_binding_corr(df):
    chart = (
        alt.Chart(df, title="Correlation Between Mutant Binding Scores")
        .mark_rect()
        .encode(
            x=alt.X(
                "binding_mean_E2",
                title="bEFNB2 binding",
                axis=alt.Axis(values=[-4,-2, 0, 2]),
            ).bin(maxbins=50),
            y=alt.Y(
                "binding_mean_E3",
                title="bEFNB3 binding",
                axis=alt.Axis(values=[-2, 0, 2]),
            ).bin(maxbins=50),
            color=alt.Color("count()", title="Count").scale(type='log'),
        )
    ).properties(width=200,height=200)
    return chart


entry_binding_corr_heatmap_1 = plot_entry_binding_corr(df_binding_effect_merge)
entry_binding_corr_heatmap_1.display()
if entry_binding_combined_corr_plot is not None:
    entry_binding_corr_heatmap_1.save(binding_corr_heatmap)

def plot_affinity_BLI_mutants(df, highlight_conditions, color,subset=None):
    df = df.dropna().round(2).copy()
    df['site_mutant'] = df['site'].astype(str) + df['mutant'].astype(str)

    if subset is not None:
        df = df[df['site'].isin(subset)]

    slope, intercept, r_value, p_value, std_err = scipy.stats.linregress(
        df["binding_mean_E2"], df["binding_mean_E3"]
    )
    
    # make correlation chart
    chart = alt.Chart(df,title=alt.Title("Correlation Between Mutant Binding Scores")
    ).mark_point(
        color="gray", 
        size=30, 
        opacity=0.4, 
        filled=True
    ).encode(
        x=alt.X("binding_mean_E2", title=("bEFNB2 binding"),axis=alt.Axis(tickCount=4)),
        y=alt.Y("binding_mean_E3", title=("bEFNB3 binding"),axis=alt.Axis(tickCount=4)),
        tooltip=[
            "site",
            "wildtype",
            "mutant",
            "binding_mean_E2",
            "binding_mean_E3",
            "effect_E2",
            "effect_E3",
        ],
    )

    #make orange circles for specific data
    highlight = chart.transform_filter(highlight_conditions).mark_point(
        color=color, size=40, opacity=1, filled=True, stroke='black', strokeWidth=1
    )
    #write text near the orange circles
    text_on_point = chart.transform_filter(highlight_conditions).mark_text(
            align='center',baseline='top',dy=-15,fontSize=14
    ).encode(text='site_mutant')
    
    min = int(df["binding_mean_E2"].min())
    max = int(df["binding_mean_E3"].max())
    text = (
        alt.Chart({"values": [{"x": min, "y": max, "text": f"r = {r_value:.2f}"}]})
        .mark_text(align="left", baseline="top", dx=-10, dy=-20)
        .encode(x=alt.X("x:Q"), y=alt.Y("y:Q"), text="text:N")
    )
    chart_and_text = chart + text_on_point + highlight #+ text
    return chart_and_text.properties(height=300,width=300)

# these are the data we want to show in orange circles
highlight_conditions = (
        (alt.datum.site == 244) & (alt.datum.mutant == 'W') |
        (alt.datum.site == 305) & (alt.datum.mutant == 'W') | 
        (alt.datum.site == 492) & (alt.datum.mutant == 'L') |
        (alt.datum.site == 507) & (alt.datum.mutant == 'I') |
        (alt.datum.site == 530) & (alt.datum.mutant == 'F') |
        (alt.datum.site == 553) & (alt.datum.mutant == 'W') |
        (alt.datum.site == 555) & (alt.datum.mutant == 'Y') |
        (alt.datum.site == 559) & (alt.datum.mutant == 'R') |
        (alt.datum.site == 588) & (alt.datum.mutant == 'V') 
)

E2_E3_corr = plot_affinity_BLI_mutants(df_binding_effect_merge,highlight_conditions,'#e49444')
E2_E3_corr.display()
if entry_binding_combined_corr_plot is not None:
    E2_E3_corr.save(E2_E3_correlation)

highlight_conditions_neut = (
        (alt.datum.site == 333) & (alt.datum.mutant == 'Q') |
        (alt.datum.site == 492) & (alt.datum.mutant == 'R') | 
        (alt.datum.site == 507) & (alt.datum.mutant == 'I') |
        (alt.datum.site == 530) & (alt.datum.mutant == 'F') |
        (alt.datum.site == 553) & (alt.datum.mutant == 'W') |
        (alt.datum.site == 555) & (alt.datum.mutant == 'K') 
)
neut_muts = plot_affinity_BLI_mutants(df_binding_effect_merge,highlight_conditions_neut,'#5778a4')
neut_muts.display()

highlight_conditions_top = (
        (alt.datum.site == 580) & (alt.datum.mutant == 'S') |
        (alt.datum.site == 598) & (alt.datum.mutant == 'G') | 
        (alt.datum.site == 492) & (alt.datum.mutant == 'L') |
        #(alt.datum.site == 553) & (alt.datum.mutant == 'W') |
        (alt.datum.site == 588) & (alt.datum.mutant == 'P') |
        (alt.datum.site == 492) & (alt.datum.mutant == 'R') |
        (alt.datum.site == 530) & (alt.datum.mutant == 'F') |
        (alt.datum.site == 492) & (alt.datum.mutant == 'K') |
        (alt.datum.site == 239) & (alt.datum.mutant == 'H') |
        #(alt.datum.site == 211) & (alt.datum.mutant == 'F') |
        (alt.datum.site == 546) & (alt.datum.mutant == 'H') |
        (alt.datum.site == 143) & (alt.datum.mutant == 'Q') |
        (alt.datum.site == 331) & (alt.datum.mutant == 'E') |
        (alt.datum.site == 589) & (alt.datum.mutant == 'G') 
)
top_muts = plot_affinity_BLI_mutants(df_binding_effect_merge,highlight_conditions_top,'#d1615d')
top_muts.display()

highlight_conditions_top_contact = (
        (alt.datum.site == 580) & (alt.datum.mutant == 'S') |
        #(alt.datum.site == 458) & (alt.datum.mutant == 'Y') | 
        (alt.datum.site == 492) & (alt.datum.mutant == 'L') |
        (alt.datum.site == 491) & (alt.datum.mutant == 'A') |
        (alt.datum.site == 588) & (alt.datum.mutant == 'P') |
        (alt.datum.site == 492) & (alt.datum.mutant == 'R') |
        (alt.datum.site == 530) & (alt.datum.mutant == 'F') |
        (alt.datum.site == 492) & (alt.datum.mutant == 'K') |
        (alt.datum.site == 239) & (alt.datum.mutant == 'H') 
)
top_muts_contact = plot_affinity_BLI_mutants(df_binding_effect_merge,highlight_conditions_top_contact,'yellow',subset = config['contact_sites'])
top_muts_contact.display()

def plot_affinity_individual_mutants(df,mutant):
    df = df.dropna().round(2).copy()
    df = df[df['site'] == mutant]
    if df.empty:
        print('nothing here')
        pass
    else:
        wildtype_site = (df['wildtype'].astype(str) + df['site'].astype(str)).unique()[0]

        chart = alt.Chart(df,title=alt.Title(f"Site {wildtype_site}")
        ).mark_text(
            size=15
        ).encode(
            x=alt.X("binding_mean_E2", title=("bEFNB2 binding"),axis=alt.Axis(tickCount=3)),
            y=alt.Y("binding_mean_E3", title=("bEFNB3 binding"),axis=alt.Axis(tickCount=3)),
            text=alt.Text('mutant'),
            color=alt.Color('mutant_type_E2',title='Amino acid type'),
            tooltip=[
                "site",
                "wildtype",
                "mutant",
                "binding_mean_E2",
                "binding_mean_E3",
                "effect_E2",
                "effect_E3",
            ],
        )
    
        return chart.properties(height=200,width=200)

indiv_mutant_589 = plot_affinity_individual_mutants(df_binding_effect_merge,589)
indiv_mutant_589.display()

indiv_mutant_553 = plot_affinity_individual_mutants(df_binding_effect_merge,553)
indiv_mutant_553.display()

indiv_mutant_580 = plot_affinity_individual_mutants(df_binding_effect_merge,580)
indiv_mutant_582 = plot_affinity_individual_mutants(df_binding_effect_merge,582)
indiv_mutant_586 = plot_affinity_individual_mutants(df_binding_effect_merge,586)
indiv_mutant_587 = plot_affinity_individual_mutants(df_binding_effect_merge,587)
indiv_mutant_589 = plot_affinity_individual_mutants(df_binding_effect_merge,589)
loop580_590 = alt.vconcat(indiv_mutant_580, indiv_mutant_582,indiv_mutant_586,indiv_mutant_587,indiv_mutant_589).resolve_scale(x='shared',y='shared',color='shared')
loop580_590.display()

indiv_mutant_172 = plot_affinity_individual_mutants(df_binding_effect_merge,172)
indiv_mutant_172.display()

empty_charts = []
for site in config['contact_sites']:
    tmp_df = df_binding_effect_merge[df_binding_effect_merge['site'] == site]
    non_nan_in_effect_E2 = tmp_df['binding_mean_E2'].notnull().any()
    non_nan_in_effect_E3 = tmp_df['binding_mean_E3'].notnull().any()

    if non_nan_in_effect_E2 and non_nan_in_effect_E3:        
        contact_plots = plot_affinity_individual_mutants(tmp_df,site)
        empty_charts.append(contact_plots)
    else:
        pass

all_contact_plots = alt.vconcat(*empty_charts).resolve_scale(x='shared', y='shared')
all_contact_plots.display()

for site in list(range(580,590)):
    tmp_df = df_binding_effect_merge[df_binding_effect_merge['site'] == site]
    non_nan_in_effect_E2 = tmp_df['binding_mean_E2'].notnull().any()
    non_nan_in_effect_E3 = tmp_df['binding_mean_E3'].notnull().any()

    if non_nan_in_effect_E2 and non_nan_in_effect_E3:        
        test_plots = plot_affinity_individual_mutants(tmp_df,site)
        test_plots.display()
    else:
        pass

def plot_affinity_solid_mean(df):
    df = df.dropna()
    means = (
        df.groupby("site")
        .agg(
            {
                "effect_E2": "mean",
                "effect_E3": "mean",
                "binding_mean_E2": "mean",
                "binding_mean_E3": "mean",
                "wildtype": "first",
            }
        )
        .reset_index().round(2)
    )
    slope, intercept, r_value, p_value, std_err = scipy.stats.linregress(
        means["binding_mean_E2"], means["binding_mean_E3"]
    )
    r_value = float(r_value)
    chart = (
        alt.Chart(
            means,
            title=alt.Title(
                "Correlation between Aggregate Mutant Binding Scores",
                subtitle=f"r={r_value:.2f}",
            ),
        )
        .mark_point(size=50, opacity=1,filled=True,color='black')
        .encode(
            x=alt.X(
                "binding_mean_E2",
                title=("Mean bEFNB2 binding"),
                axis=alt.Axis(tickCount=3),
            ),
            y=alt.Y(
                "binding_mean_E3",
                title=("Mean bEFNB3 binding"),
                axis=alt.Axis(tickCount=3),
            ),
            tooltip=[
                "site",
                "wildtype",
                "binding_mean_E2",
                "binding_mean_E3",
                "effect_E2",
                "effect_E3",
            ],
        )
    )
    text = (
        alt.Chart({"values": [{"x": -3.5, "y": 0.5, "text": f"r = {r_value:.2f}"}]})
        .mark_text(align="left", baseline="top", dx=0, dy=0)
        .encode(x=alt.X("x:Q"), y=alt.Y("y:Q"), text="text:N")
    )
    chart_and_text = chart.properties(width=300,height=300)
    return chart_and_text


E2_E3_site_corr = plot_affinity_solid_mean(df_binding_effect_merge)
E2_E3_site_corr.display()
if entry_binding_combined_corr_plot is not None:
    E2_E3_site_corr.save(E2_E3_correlation_site)

def plot_affinity_by_site(df):
     # define ranges of different RBP regions
    barrel_ranges = {
        "Stalk": list(range(70, 148)),
        "Neck": list(range(148, 166)),
        "Linker": list(range(166, 178)),
        "Head": list(range(178, 603)),
    }
    
    custom_order = ["Stalk", "Neck", "Linker", "Head"] #custom order for color legend
    
    agg_means = [] #store aggregation 
    
    # For each barrel, filter the dataframe to the sites belonging to that barrel and then store the means
    for barrel, sites in barrel_ranges.items():
        subset = df[df["site"].isin(sites)]
        for _, row in subset.iterrows():
            agg_means.append(
                {"region": barrel, "binding_mean": row["binding_mean"], "site": row["site"],"selection": row["selection"]}
            )
        agg_means_df = pd.DataFrame(agg_means).round(2)
    
    # Setup interactivity
    variant_selector = alt.selection_point(
        on="mouseover", nearest=True, empty=False, fields=["site"], value=0
    )
    #make chart
    chart = alt.Chart(agg_means_df).mark_bar(stroke='black',size=1.5,binSpacing=0,color='black').encode(
        alt.X("site")
            .title("Site")
            .axis(tickCount=5,labelAngle=-90,grid=True),
            #.scale(domain=[70, 602]),
        
        alt.Y('mean(binding_mean)')
            .axis(tickCount=3)
            .title('Mean binding'),
        alt.Row('selection',title=None),
        tooltip=['site'],
        strokeWidth=alt.condition(variant_selector, alt.value(1), alt.value(0)),
        color=alt.Color('region',sort=custom_order,title='Region',scale=alt.Scale(range=['#5778a4', '#e49444', '#d1615d', '#85b6b2'])),
        #strokeColor=alt.condition(variant_selector, alt.value('orange'), alt.value('black')),


    ).properties(height=150, width=800).add_params(variant_selector).resolve_scale(y='independent')
    

    return chart


binding_by_site = plot_affinity_by_site(df_binding_effect_concat)
binding_by_site.display()
if entry_binding_combined_corr_plot is not None:
    binding_by_site.save(binding_by_site_plot)

def plot_affinity_by_contact_site(df, sites_to_show):
    # Filter the DataFrame based on the sites to show
    contact_df = df[df["site"].isin(sites_to_show)]
    
    # Define a selection for highlighting bars on hover
    variant_selector = alt.selection_point(on="mouseover", nearest=True, empty=False, fields=["site"])
    
    # Create the chart
    chart = alt.Chart(contact_df).mark_bar(stroke='black').encode(
        y=alt.Y("site:N", title="Site",sort='-x'),
        x=alt.X("max(binding_mean):Q", title="Max binding mutant at site"),
        color=alt.condition(variant_selector, alt.value("orange"), alt.value("black")),
        strokeWidth=alt.condition(variant_selector, alt.value(1), alt.value(0)),
        column=alt.Column('selection',title=None)
    ).add_params(variant_selector).properties(width=200, height=alt.Step(12)).resolve_scale(x='independent',y='shared').configure_header(
        labelFontSize=20,  
        labelAngle=0,
        labelAlign='center',
        labelAnchor='middle',
        labelFont='Helvetica Light',
        labelFontStyle='bold',
        labelPadding=3
    )
    
    return chart

contact_binding_by_site = plot_affinity_by_contact_site(df_binding_effect_concat, config["contact_sites"])
contact_binding_by_site.display()
if entry_binding_combined_corr_plot is not None:
    contact_binding_by_site.save(combined_contact_ranked_bar_output)

def make_bubble_binding_region(df):
    barrel_ranges = {
        "Stalk": list(range(70, 148)),
        "Neck": list(range(148, 166)),
        "Linker": list(range(166, 178)),
        "Head": list(range(178, 603)),
        "Receptor Contact": config["contact_sites"],
    }
    custom_order = ["Stalk", "Neck", "Linker", "Head", "Receptor Contact"]
    agg_means = []

    # For each barrel, filter the site_means dataframe to the sites belonging to that barrel and then store the means
    for barrel, sites in barrel_ranges.items():
        subset = df[df["site"].isin(sites)]
        for _, row in subset.iterrows():
            agg_means.append(
                {
                    "region": barrel,
                    "binding_mean": row["binding_mean"],
                    "site": row["site"],
                    "mutant": row["mutant"],
                    "selection": row["selection"],
                }
            )
        agg_means_df = pd.DataFrame(agg_means)

    variant_selector = alt.selection_point(
        on="mouseover", empty=False, nearest=True, fields=["site", "mutant"], value=1
    )

    chart = alt.Chart(agg_means_df).mark_point(stroke="black",filled=True).encode(
        x=alt.X(
            "region:O",
            sort=custom_order,
            title="RBP Region",
            axis=alt.Axis(labelAngle=-90),
        ),
        y=alt.Y(
            "binding_mean",
            title="Binding",
            axis=alt.Axis(tickCount=4),
        ),
        xOffset="random:Q",
        tooltip=["region", "binding_mean", "site", "mutant"],
        color=alt.condition(
            variant_selector, alt.value("orange"), alt.value("black")
        ),
        opacity=alt.condition(variant_selector, alt.value(1), alt.value(0.1)),
        strokeWidth=alt.condition(variant_selector, alt.value(2), alt.value(0)),
        size=alt.condition(variant_selector, alt.value(70), alt.value(20)),
        column=alt.Column('selection',title=None)
    ).transform_calculate(random="sqrt(-1*log(random()))*cos(2*PI*random())"
    ).properties(
        width=300, 
        height=300
    ).add_params(variant_selector
    ).resolve_scale(y='independent'
    ).configure_header(
        labelFontSize=20,  
        labelAngle=0,
        labelAlign='center',
        labelAnchor='middle',
        labelFont='Helvetica Light',
        labelFontStyle='bold',
        labelPadding=1
    )
    
    return chart


entry_region_bubble = make_bubble_binding_region(df_binding_effect_concat)
entry_region_bubble.display()
if entry_binding_combined_corr_plot is not None:
    entry_region_bubble.save(binding_region_bubble_plot)

display(df_binding_effect_concat)

def plot_effects_of_mutants(df):
    tmp_df = df.copy()
    tmp_df = tmp_df[
        #((tmp_df['site'] == 580) & (tmp_df['mutant'] == 'S')) |
        ((tmp_df['site'] == 598) & (tmp_df['mutant'] == 'G')) |
        #((tmp_df['site'] == 492) & (tmp_df['mutant'] == 'L')) |
        ((tmp_df['site'] == 553) & (tmp_df['mutant'] == 'W')) |
        #((tmp_df['site'] == 588) & (tmp_df['mutant'] == 'P')) |
        #((tmp_df['site'] == 492) & (tmp_df['mutant'] == 'R')) |
        #((tmp_df['site'] == 530) & (tmp_df['mutant'] == 'F')) |
        #((tmp_df['site'] == 492) & (tmp_df['mutant'] == 'K')) |
        #((tmp_df['site'] == 239) & (tmp_df['mutant'] == 'H')) |
        ((tmp_df['site'] == 211) & (tmp_df['mutant'] == 'F')) |
        ((tmp_df['site'] == 546) & (tmp_df['mutant'] == 'H')) |
        ((tmp_df['site'] == 143) & (tmp_df['mutant'] == 'Q')) |
        ((tmp_df['site'] == 331) & (tmp_df['mutant'] == 'E')) |
        ((tmp_df['site'] == 566) & (tmp_df['mutant'] == 'C')) 

        #((tmp_df['site'] == 589) & (tmp_df['mutant'] == 'G')) 
    ]
    tmp_df['label'] = (tmp_df['wildtype'].astype(str) + tmp_df['site'].astype(str) + tmp_df['mutant'].astype(str))

    tmp_df = tmp_df.sort_values(by='site')
    binding_chart = alt.Chart(tmp_df).mark_bar().encode(
        alt.X("label:N",title='Mutant',sort=None),
        alt.Y("binding_mean:Q",title='Binding score',axis=alt.Axis(tickCount=4)),
        #xOffset="selection:N",
        color=alt.Color("selection:N",title='Receptor selection'),
        row='selection'
    )
    binding_chart.resolve_scale(y='independent',x='shared').display()
plot_effects_of_mutants(df_binding_effect_concat)

	site	wildtype	mutant	binding_mean	binding_std	times_seen_binding	effect	effect_std	times_seen_cell_entry	frac_models	mutant_type
0	71	Q	D	-0.7817	1.6460	3.25	-1.164	0.8890	4.500	1.0	negative
1	71	Q	E	0.1659	0.4309	3.50	-1.255	0.3123	5.375	1.0	negative
2	71	Q	F	-0.3429	0.5299	3.00	-1.058	0.6637	4.625	1.0	aromatic

	site	wildtype	mutant	binding_mean	binding_std	times_seen_binding	effect	effect_std	times_seen_cell_entry	frac_models	mutant_type
0	71	Q	C	-0.1332	0.1525	3.0	-0.7227	0.7828	3.000	1.0	special
1	71	Q	D	-0.2937	0.3305	4.0	-0.3884	0.6369	3.429	1.0	negative
2	71	Q	E	-0.3527	0.1762	4.0	-0.2482	0.9791	4.571	1.0	negative

	site	wildtype	mutant	binding_mean_E2	binding_std_E2	times_seen_binding_E2	effect_E2	effect_std_E2	times_seen_cell_entry_E2	frac_models_E2	mutant_type_E2	binding_mean_E3	binding_std_E3	times_seen_binding_E3	effect_E3	effect_std_E3	times_seen_cell_entry_E3	frac_models_E3	mutant_type_E3
0	71	Q	D	-0.7817	1.6460	3.25	-1.164	0.8890	4.500	1.0	negative	-0.29370	0.3305	4.0	-0.3884	0.6369	3.429	1.0	negative
1	71	Q	E	0.1659	0.4309	3.50	-1.255	0.3123	5.375	1.0	negative	-0.35270	0.1762	4.0	-0.2482	0.9791	4.571	1.0	negative
2	71	Q	F	-0.3429	0.5299	3.00	-1.058	0.6637	4.625	1.0	aromatic	-0.03982	0.1283	3.0	-0.4973	0.3080	3.286	1.0	aromatic

	site	wildtype	mutant	binding_mean	binding_std	times_seen_binding	effect	effect_std	times_seen_cell_entry	frac_models	mutant_type	selection
0	71	Q	D	-0.7817	1.6460	3.25	-1.164	0.8890	4.500	1.0	negative	bEFNB2
1	71	Q	E	0.1659	0.4309	3.50	-1.255	0.3123	5.375	1.0	negative	bEFNB2
2	71	Q	F	-0.3429	0.5299	3.00	-1.058	0.6637	4.625	1.0	aromatic	bEFNB2

	site	wildtype	mutant	binding_mean	binding_std	times_seen_binding	effect	effect_std	times_seen_cell_entry	frac_models	mutant_type	selection
5484	501	E	K	-5.280	0.9284	24.50	-0.6440	0.6061	32.00	1.0	positive	bEFNB2
5861	532	A	V	-5.259	1.1400	16.25	-0.3199	0.3122	17.88	1.0	hydrophobic	bEFNB2
1817	236	R	H	-5.054	0.8828	22.00	-1.2530	0.3352	29.38	1.0	positive	bEFNB2
5354	490	Q	K	-4.966	1.0810	8.50	-0.6916	0.5391	11.38	1.0	positive	bEFNB2
5352	490	Q	H	-4.752	0.8619	8.25	-0.4499	0.6679	10.88	1.0	positive	bEFNB2

ephrin_binding.ipynb¶

hard paths for running in interactive mode¶

Run config files to setup altair theme and config variables¶

Import the filtered binding and entry data for bEFNB2 and bEFNB3¶

Merge data¶

Find the top overlapping binders for both bEFNB2 and bEFNB3¶

Make plots showing correlation between binding and entry for EFNB2 and EFNB3¶

Same plot as above, but slightly different format¶

Calculate some stats on binding¶

Find sites with opposite effects on binding¶

Find correlations between bEFNB2 and bEFNB3 binding¶

Plot correlations of binding mutants in scatterplots, and color different subsets¶

Sites selected for BLI validation¶

Sites selected for neutralization validations¶

Top sites different in bEFNB2/3¶

Same as above, top sites different in bEFNB2/3 but only for contact sites¶

Plot correlations of mutants for individual sites of interest with letters of each mutant plotted¶

Now make for all contact sites¶

Plot correlations between each site for mean value¶

Make plot showing binding by site¶

Make bar chart showing max binding score for each contact residue¶

Make bubble plots for binding in different areas of receptor pocket¶

Plot effects of selected mutants on cell entry¶

	site	wildtype	mutant	binding_mean	binding_std	times_seen_binding	effect	effect_std	times_seen_cell_entry	frac_models	mutant_type	selection
6308	566	F	C	2.160	2.1190	5.00	-0.5326	0.4547	5.500	0.75	special	bEFNB2
6491	580	I	S	2.152	1.7680	4.75	-1.0010	0.6921	6.375	1.00	hydrophilic	bEFNB2
3054	331	N	E	2.144	1.2330	3.00	-0.8872	0.8436	3.500	1.00	negative	bEFNB2
2111	269	N	E	2.075	2.2410	5.00	-1.0480	0.4835	7.875	1.00	negative	bEFNB2
6596	586	N	T	1.962	0.5779	5.25	-0.8163	0.8231	7.000	1.00	hydrophilic	bEFNB2

	site	wildtype	mutant	binding_mean	binding_std	times_seen_binding	effect	effect_std	times_seen_cell_entry	frac_models	mutant_type	selection
6225	558	A	V	-4.647	0.7295	17.25	0.1182	0.3640	18.50	1.0	hydrophobic	bEFNB2
6211	557	N	D	-4.627	0.3989	9.50	0.3848	0.4791	11.12	1.0	negative	bEFNB2
5771	526	L	H	-4.546	0.7822	5.25	0.1102	0.3920	6.75	1.0	positive	bEFNB2
6510	581	Y	W	-4.519	1.0130	10.75	0.0907	0.4101	11.50	1.0	aromatic	bEFNB2
5367	491	S	H	-4.505	0.9350	8.50	0.1124	0.2502	10.25	1.0	positive	bEFNB2

	site	wildtype	mutant	binding_mean	binding_std	times_seen_binding	effect	effect_std	times_seen_cell_entry	frac_models	mutant_type	selection
2334	282	C	S	1.609	1.056	4.50	0.3378	0.3251	3.714	0.50	hydrophilic	bEFNB2
4833	450	Q	I	1.457	1.194	5.00	0.1418	0.3988	6.000	1.00	hydrophobic	bEFNB2
3193	339	S	A	1.389	2.087	2.00	0.4493	0.1488	2.625	0.75	hydrophobic	bEFNB2
3522	361	T	A	1.371	2.108	3.50	0.3626	0.4499	4.875	1.00	hydrophobic	bEFNB2
817	164	N	S	1.297	1.136	3.75	0.2398	0.2906	6.125	1.00	hydrophilic	bEFNB2

	site	binding_mean
250	331	15.73280
473	564	15.08690
97	172	13.88370
225	306	13.85177
493	586	13.36944
103	178	9.99930
151	227	9.97030
90	165	8.74900
239	320	8.57420
496	589	8.56552

	site	wildtype	mutant	binding_mean	binding_std	times_seen_binding	effect	effect_std	times_seen_cell_entry	frac_models	mutant_type	selection
6124	555	D	K	-2.147	1.2250	6.0	0.08504	0.4221	6.714	1.0	positive	bEFNB3
6127	555	D	R	-1.510	0.4205	4.0	0.07591	0.5260	4.143	1.0	positive	bEFNB3
5810	530	Q	L	-1.447	0.4772	3.5	-1.01900	0.8623	5.857	1.0	hydrophobic	bEFNB3
6413	583	T	R	-1.410	0.4465	5.5	-1.00500	0.4854	6.286	1.0	positive	bEFNB3
6410	583	T	K	-1.247	0.1132	5.0	-0.72780	0.6441	7.000	1.0	positive	bEFNB3

	site	wildtype	mutant	binding_mean	binding_std	times_seen_binding	effect	effect_std	times_seen_cell_entry	frac_models	mutant_type	selection
6460	589	R	G	2.006	0.65910	7.0	-0.9655	0.5763	8.286	1.0	special	bEFNB3
6379	580	I	M	1.337	0.53380	5.5	-0.8155	0.5307	6.714	1.0	hydrophobic	bEFNB3
2361	270	V	Q	1.329	1.02800	5.0	-0.8846	0.6509	5.429	1.0	hydrophilic	bEFNB3
2313	267	M	Q	1.291	1.37100	4.5	-0.7981	0.8175	5.667	1.0	hydrophilic	bEFNB3
5462	492	Q	L	1.261	0.06666	5.5	0.4833	0.1827	5.143	1.0	hydrophobic	bEFNB3

	site	wildtype	mutant	binding_mean	binding_std	times_seen_binding	effect	effect_std	times_seen_cell_entry	frac_models	mutant_type	selection
6124	555	D	K	-2.1470	1.2250	6.0	0.08504	0.42210	6.714	1.0	positive	bEFNB3
6127	555	D	R	-1.5100	0.4205	4.0	0.07591	0.52600	4.143	1.0	positive	bEFNB3
5813	530	Q	W	-0.9974	0.5473	3.0	0.01818	0.66470	3.286	1.0	aromatic	bEFNB3
6118	555	D	A	-0.8295	0.1999	3.5	0.37550	0.23450	3.714	1.0	hydrophobic	bEFNB3
4852	441	P	V	-0.7461	0.1815	5.5	0.37080	0.08612	5.286	1.0	hydrophobic	bEFNB3

	site	wildtype	mutant	binding_mean	binding_std	times_seen_binding	effect	effect_std	times_seen_cell_entry	frac_models	mutant_type	selection
5462	492	Q	L	1.2610	0.06666	5.5	0.48330	0.1827	5.143	1.0	hydrophobic	bEFNB3
1708	211	G	F	1.1690	0.28900	5.0	0.16370	0.5716	5.714	1.0	aromatic	bEFNB3
6543	598	P	G	1.1540	1.55900	4.5	0.23870	0.5146	5.429	1.0	special	bEFNB3
6098	553	S	W	0.9943	0.43520	9.5	0.16690	0.4060	9.857	1.0	aromatic	bEFNB3
6402	582	D	W	0.9761	0.33840	6.5	0.06342	0.3096	7.143	1.0	aromatic	bEFNB3

	site	binding_mean
490	589	11.707770
87	161	7.332800
469	564	6.721080
225	306	6.492740
59	132	6.168804
242	323	6.101950
470	566	5.968996
261	342	5.728920
129	204	5.653570
175	254	5.152500

	site	wildtype	mutant	binding_mean_E2_x	binding_std_E2_x	times_seen_binding_E2_x	effect_E2_x	effect_std_E2_x	times_seen_cell_entry_E2_x	frac_models_E2_x	...	frac_models_E2_y	mutant_type_E2_y	binding_mean_E3_y	binding_std_E3_y	times_seen_binding_E3_y	effect_E3_y	effect_std_E3_y	times_seen_cell_entry_E3_y	frac_models_E3_y	mutant_type_E3_y
0	566	F	C	2.160	2.1190	5.000	-0.5326	0.4547	5.500	0.75	...	0.75	special	1.1320	1.194000	3.5	-0.8507	0.7599	3.833	1.0	special
1	580	I	S	2.152	1.7680	4.750	-1.0010	0.6921	6.375	1.00	...	1.00	hydrophilic	0.8187	0.030890	6.0	0.1163	0.3235	5.571	1.0	hydrophilic
2	211	G	F	1.957	0.5488	4.750	-0.6654	0.4262	5.125	1.00	...	1.00	aromatic	1.1690	0.289000	5.0	0.1637	0.5716	5.714	1.0	aromatic
3	553	S	W	1.945	0.2867	10.000	-0.2967	0.4293	13.250	1.00	...	1.00	aromatic	0.9943	0.435200	9.5	0.1669	0.4060	9.857	1.0	aromatic
4	139	N	W	1.930	0.4834	5.750	-0.6672	0.6217	8.000	1.00	...	1.00	aromatic	1.0030	0.004575	4.5	-0.4441	0.4452	6.286	1.0	aromatic
5	589	R	G	1.668	0.9018	7.333	-1.3800	0.4560	11.880	0.75	...	0.75	special	2.0060	0.659100	7.0	-0.9655	0.5763	8.286	1.0	special

	site	wildtype	mutant	binding_mean_E2	binding_std_E2	times_seen_binding_E2	effect_E2	effect_std_E2	times_seen_cell_entry_E2	frac_models_E2	mutant_type_E2	binding_mean_E3	binding_std_E3	times_seen_binding_E3	effect_E3	effect_std_E3	times_seen_cell_entry_E3	frac_models_E3	mutant_type_E3	binding_diff
5835	530	Q	F	-4.015	0.46650	6.25	0.46740	0.2023	5.375	1.0	aromatic	0.084370	0.06198	6.0	0.3592	0.2738	6.286	1.0	aromatic	4.099370
5362	490	Q	Y	-4.290	0.90020	5.50	-1.08800	0.4004	6.375	1.0	aromatic	-0.461000	0.57400	5.5	-0.3137	0.7967	5.714	1.0	aromatic	3.829000
5367	491	S	H	-4.505	0.93500	8.50	0.11240	0.2502	10.250	1.0	positive	-0.871800	0.78980	7.0	-1.3810	0.4865	8.286	1.0	positive	3.633200
5366	491	S	G	-4.181	0.59490	6.50	0.13020	0.2975	8.625	1.0	special	-0.562100	0.04726	5.5	-0.2358	0.6084	6.286	1.0	special	3.618900
5361	490	Q	W	-4.205	1.00500	3.75	0.23460	0.3779	4.750	1.0	aromatic	-0.586500	0.32260	3.0	-0.1506	0.4575	3.286	1.0	aromatic	3.618500
6627	588	I	P	-2.349	0.84050	5.25	-0.25550	0.3004	4.750	1.0	special	1.183000	0.25530	5.5	-0.6131	0.3819	5.429	1.0	special	3.532000
5385	492	Q	K	-3.523	0.54860	11.00	0.35270	0.2320	11.620	1.0	positive	0.006126	0.07740	10.0	0.3291	0.2404	11.570	1.0	positive	3.529126
5389	492	Q	R	-2.823	0.61110	19.25	-0.07662	0.2715	23.120	1.0	positive	0.643700	0.05388	20.0	0.5095	0.1192	20.570	1.0	positive	3.466700
1864	239	S	H	-3.440	0.08816	5.25	0.33410	0.2721	4.500	1.0	positive	-0.041160	0.83220	4.0	-1.2190	0.6930	5.000	1.0	positive	3.398840
5844	530	Q	V	-4.327	0.83770	6.00	0.10570	0.2693	8.625	1.0	hydrophobic	-0.987000	0.89600	5.5	-1.4430	0.7389	7.000	1.0	hydrophobic	3.340000

	site	binding_mean_E2	binding_mean_E3	binding_diff
416	505	-3.790625	-0.770900	2.897100
440	530	-3.631593	-0.559266	2.797646
406	491	-3.575407	-0.364667	2.785117
496	588	-3.474250	0.037600	2.686600
408	494	-3.915765	-0.615000	2.411333

	site	wildtype	mutant	binding_mean_E2	binding_std_E2	times_seen_binding_E2	effect_E2	effect_std_E2	times_seen_cell_entry_E2	frac_models_E2	mutant_type_E2	binding_mean_E3	binding_std_E3	times_seen_binding_E3	effect_E3	effect_std_E3	times_seen_cell_entry_E3	frac_models_E3	mutant_type_E3	binding_diff
6041	546	L	H	1.5230	1.1450	5.25	-1.1480	0.3866	6.750	1.0	positive	-0.5431	0.6654	3.5	-0.85080	0.5440	5.143	1.0	positive	2.0661
2620	303	P	C	1.3450	1.5770	5.00	-0.7175	0.5900	5.000	1.0	special	-0.5659	0.0975	4.0	-0.69680	0.5942	4.857	1.0	special	1.9109
122	79	N	S	1.2920	1.6520	3.00	-0.6352	0.4964	2.375	1.0	hydrophilic	-0.6464	0.2595	3.0	-0.12940	0.5688	3.429	1.0	hydrophilic	1.9384
103	78	D	I	0.6586	0.6340	5.25	-0.9306	0.3975	6.500	1.0	hydrophobic	-0.5665	0.7552	5.0	-0.51670	0.8447	5.143	1.0	hydrophobic	1.2251
1681	224	M	Q	0.6473	0.8796	3.50	0.1659	0.3148	2.750	1.0	hydrophilic	-0.5806	0.3813	3.5	0.20550	0.2920	4.286	1.0	hydrophilic	1.2279
5707	520	I	G	0.5914	1.0130	5.00	-1.4650	0.3487	8.000	1.0	special	-0.5305	0.1476	5.5	-1.30900	0.3187	7.000	1.0	special	1.1219
1031	178	V	T	0.5288	0.7257	3.75	-0.5276	0.7591	4.250	1.0	hydrophilic	-0.5130	0.5995	3.0	-0.01921	0.4254	3.571	1.0	hydrophilic	1.0418

	site	wildtype	mutant	binding_mean_E2	binding_std_E2	times_seen_binding_E2	effect_E2	effect_std_E2	times_seen_cell_entry_E2	frac_models_E2	mutant_type_E2	binding_mean_E3	binding_std_E3	times_seen_binding_E3	effect_E3	effect_std_E3	times_seen_cell_entry_E3	frac_models_E3	mutant_type_E3	binding_diff
6627	588	I	P	-2.3490	0.8405	5.25	-0.25550	0.3004	4.750	1.0	special	1.1830	0.255300	5.5	-0.6131	0.3819	5.429	1.0	special	3.5320
6733	598	P	G	-0.5523	0.7852	5.00	-0.86740	0.9393	4.625	1.0	special	1.1540	1.559000	4.5	0.2387	0.5146	5.429	1.0	special	1.7063
5236	479	W	S	-0.8017	1.2720	3.25	-1.30400	0.8784	5.250	1.0	hydrophilic	0.7307	2.957000	2.0	-1.4690	0.6120	3.286	1.0	hydrophilic	1.5324
3179	338	R	G	-0.5172	0.5068	4.50	0.29960	0.4223	5.250	1.0	special	0.6836	0.975700	4.0	0.1580	0.3395	3.429	1.0	special	1.2008
5363	491	S	A	-0.9317	0.5371	5.50	-0.66860	0.8056	6.250	1.0	hydrophobic	0.6451	0.009846	6.5	0.1429	0.3582	6.143	1.0	hydrophobic	1.5768
5389	492	Q	R	-2.8230	0.6111	19.25	-0.07662	0.2715	23.120	1.0	positive	0.6437	0.053880	20.0	0.5095	0.1192	20.570	1.0	positive	3.4667

	site	wildtype	mutant	binding_mean	binding_std	times_seen_binding	effect	effect_std	times_seen_cell_entry	frac_models	mutant_type	selection
0	71	Q	D	-0.78170	1.646000	3.25	-1.1640	0.88900	4.500	1.0	negative	bEFNB2
1	71	Q	E	0.16590	0.430900	3.50	-1.2550	0.31230	5.375	1.0	negative	bEFNB2
2	71	Q	F	-0.34290	0.529900	3.00	-1.0580	0.66370	4.625	1.0	aromatic	bEFNB2
3	71	Q	G	0.46570	0.704200	4.00	-1.4250	0.58780	7.875	1.0	special	bEFNB2
4	71	Q	H	0.02003	0.405500	2.50	-0.3764	0.36300	2.750	1.0	positive	bEFNB2
...	...	...	...	...	...	...	...	...	...	...	...	...
6613	602	T	R	-0.04309	0.119100	5.00	0.4114	0.18240	6.286	1.0	positive	bEFNB3
6614	602	T	S	0.08546	0.005451	5.00	0.3778	0.15710	3.429	1.0	hydrophilic	bEFNB3
6615	602	T	V	0.20070	0.101500	6.00	0.4051	0.13370	5.429	1.0	hydrophobic	bEFNB3
6616	602	T	W	0.16630	0.134300	6.50	0.5205	0.09616	6.857	1.0	aromatic	bEFNB3
6617	602	T	Y	0.05261	0.016170	6.50	0.4106	0.23930	6.143	1.0	aromatic	bEFNB3